A New Method for State of Charge and Capacity Estimation of Lithium-Ion Battery Based on Dual Strong Tracking Adaptive H Infinity Filter

Zheng Liu,Xuanju Dang

doi:10.1155/2018/5218205

Abstract

As one of the most important features representing the operating state of power battery in electric vehicles (EVs), state of charge (SOC) and capacity estimation is a crucial assessment index in battery management system (BMS). This paper presents a fusion method of SOC and capacity estimation with identified model parameters. The equivalent circuit model (ECM) parameters are obtained online by variable forgetting factor recursive least squares (VFFRLS), which is based on incremental ECM analysis to respond to the inconsistent rates of parameters variation. The independent open-circuit voltage (OCV) estimation way is designed to reduce the effect of mutual coupling between OCV and ECM parameters. Based on the identified ECM parameters and OCV, a dual adaptive H infinity filter (AHIF) combined with strong tracking filter (STF) is proposed to estimate battery SOC and capacity. A new quadratic function as capacity error compensation is introduced to represent the relationship between capacity and OCV. The adaptive strategy of the AHIF can adjust noise covariance and restricted factor, while the STF can regulate prior state covariance by adding suboptimum fading factor. The results of experiment and simulation show the merits of proposed approach in SOC and capacity estimation.

Highlights

For the sustainable development strategy of electric vehicles (EVs), the rechargeable lithium-ion battery has been extensively investigated in battery management system (BMS) for EVs in recent years [1, 2]
The ST-adaptive H infinity filter (AHIF) has advantages on the issue of restraining model uncertainty and abrupt fluctuation compared with AHIF, the error tends to increase, the error of capacity estimation close to 4% in the special areas corresponding to larger state of charge (SOC) estimation error, which is beyond the acceptable range
The capacity estimation error obtained by error compensation (EC) in [47] can remain within 1% while the capacity estimation error got by improved EC is kept in 0.5% no matter whether in AHIF or strong tracking AHIF (ST-AHIF)

Summary

Introduction

For the sustainable development strategy of electric vehicles (EVs), the rechargeable lithium-ion battery has been extensively investigated in BMS for EVs in recent years [1, 2]. Since lithium-ion battery can be thought of as a strong nonlinear system with complex electrochemical characteristics, the SOC and capacity cannot directly measured by sensors, while they can be estimated by utilizing ECM-based mathematical method and so on. Because the battery’s steady state cannot be achieved until the long rest time, OCV method is not suitable for real-time SOC estimation. Another approach is the Ampere-Hour counting (AH) method [9, 10] with speed calculation and simple implementation. The black box-based forecasting technique, such as the artificial neural network (ANN) [11, Mathematical Problems in Engineering

Methods

Results

Discussion

Conclusion